A robust model for determining the mesh stiffness of cylindrical gears

2015 ◽  
Vol 87 ◽  
pp. 93-114 ◽  
Author(s):  
Lehao Chang ◽  
Geng Liu ◽  
Liyan Wu
2020 ◽  
Vol 64 (4) ◽  
pp. 289-298
Author(s):  
Dániel Debreczeni ◽  
Gabriella Bognár

The calculation of mesh stiffness with required accuracy is essential for determining the contact characteristics of gear pairs. The easiest approximation of the relative stiffness for the basic profile geometries is the so-called single stiffness. Standardized and analytical methods for the determination of the single and mesh stiffness of gears are used to achieve design goals considering the load capacity and the vibration excitation characteristics. Such methods involve the formulas of ISO 6336-1:2006 based on experimental relationships and the equations of Weber and Banaschek based on mechanical calculations. In this paper, guidelines are given to refine the analytical calculations. Our goal is to present the impact of the change of the applied pressure angle, module, load, rim thickness and tooth number on the maximal single stiffness. The profile geometry of the gears is generated with our program in MATLAB. The profile of gears is calculated by the tool geometry and the kinematics of production. The geometry is imported into Abaqus. The sensitivity of the models to different parameters is examined and compared to those obtained by analytical calculations. The benchmarks for the single stiffness are the two most widely used analytical calculation methods in Europe such as ISO 6336-1:2006 formulas and Weber and Banaschek equations.


2021 ◽  
Vol 166 ◽  
pp. 104472
Author(s):  
João D.M. Marafona ◽  
Pedro M.T. Marques ◽  
Ramiro C. Martins ◽  
Jorge H.O. Seabra

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.


2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.


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